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Deep Learning Gene Regulatory Networ...

Maulding, Nathan,

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Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning Gene Regulatory Network Dynamics From Transcriptomes // Nathan Maulding.
Author:	Maulding, Nathan,
Description:	1 electronic resource (181 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
Subject:	Bioinformatics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31329684
ISBN:	9798384010029

Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /
Maulding, Nathan,

Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /Nathan Maulding. - 1 electronic resource (181 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Regulation of gene expression is critical for all life processes. RNAseq allows for the investigation of the expression state and the inference of gene regulatory networks (GRNs). The GRN reveals mechanisms of disease and development both at an organismal and cellular level. Constructing GRNs from expression data is done by detecting statistical relationships over a series of samples. However, these relationships are noisy and do not take into account many important variables. To explore one such limitation, I implemented dual RNAseq to investigate the regulatory mechanisms that occur between both host and virus transcriptomes in SARS-CoV-2 infected samples (Chapter 3). To address the temporal aspect of GRNs, I developed a statistical framework for detecting relationships between transcription factors and their targets from inferred trajectory progressions ("pseudotime") in single-cells (Chapter 4). Finally, I developed an interpretable, time-aware deep learning transformer for modeling disease progression and demonstrate its ability to identify gene programs that promote breast cancer resistance post-treatment (Chapter 5).

English

ISBN: 9798384010029Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Deep learning

Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /
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520 $a Regulation of gene expression is critical for all life processes. RNAseq allows for the investigation of the expression state and the inference of gene regulatory networks (GRNs). The GRN reveals mechanisms of disease and development both at an organismal and cellular level. Constructing GRNs from expression data is done by detecting statistical relationships over a series of samples. However, these relationships are noisy and do not take into account many important variables. To explore one such limitation, I implemented dual RNAseq to investigate the regulatory mechanisms that occur between both host and virus transcriptomes in SARS-CoV-2 infected samples (Chapter 3). To address the temporal aspect of GRNs, I developed a statistical framework for detecting relationships between transcription factors and their targets from inferred trajectory progressions ("pseudotime") in single-cells (Chapter 4). Finally, I developed an interpretable, time-aware deep learning transformer for modeling disease progression and demonstrate its ability to identify gene programs that promote breast cancer resistance post-treatment (Chapter 5).
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